There are a number of requirements for on-board diagnostics II (OBDII) of the California Air Resources Board (CARB). Once such requirement is the ability to detect misfires in the engine in order to identify a malfunction.
U.S. Pat. No. 5,200,899 noted above discloses instrumentation and computational procedures for detecting misfires in automotive engines such as is required by California Air Resources on-board diagnostic-II regulations.
Whereas normal engine combustion involves a certain level of torque nonuniformity, misfire significantly increases the nonuniformity. This nonuniformity can be readily observed in the frequency domain. Furthermore, the torque nonuniformity results in a nonuniformity in crankshaft angular speed.
The above-noted U.S. Pat. No. 5,487,008 discloses computational methods in which samples of crankshaft angular speed are obtained. Selected components for each complete engine cycle (i.e., crankshaft revolutions) of the spectrum are obtained using a suitable method (e.g. Fast Fourier Transform (FFT)).
Normally, the Fourier component at engine cycle frequency is sufficient to detect misfires although other components can be useful for certain powertrain configurations (e.g., the component at cylinder firing frequency). For this spectrum computation, the input sequence {w.sub.k } is obtained by sampling crankshaft angular speed w at uniformly spaced intervals of crankshaft angular position .theta.: ##EQU1## Various methods are available for noncontacting measurement of w.sub.k (as disclosed in U.S. Pat. No. 5,200,899).
Then, the pth Fourier complex amplitude A.sub.p is computed using a suitable algorithm. For example (although it is not computationally efficient), there is the well known discrete Fourier transform (DFT): ##EQU2## The complex amplitude at engine cycle frequency is A.sub.1. EQU A.sub.1 =M.sub.1 e.sup.j.phi.1
where M.sub.1 is the amplitude and .phi..sub.1 is the phase.
According to U.S. Pat. No. 5,200,899, misfire can be detected by applying a decision algorithm to this (these) component(s). For example, one such criterion is a simple threshold comparison of the amplitude of the engine cycle frequency (M.sub.1). EQU M.sub.1 &lt;M.sub.T .fwdarw.no misfire EQU M.sub.1 &gt;M.sub.T .fwdarw.misfire
where M.sub.T is the threshold value. Alternatively, a decision algorithm in the M.sub.1, .phi..sub.1 plane can be used as disclosed in the above U.S. Pat. No. 5,487,008.
The above-noted U.S. Pat. No. 5,495,415 discloses misfire detection in automotive engines (primarily gasoline fueled, spark ignited reciprocating engines) and includes the steps of:
(1) obtaining noncontacting measurement of crankshaft angular speed; PA1 (2) obtaining a sequence of samples of these measurements for an interval including one or more complete engine cycles; PA1 (3) computing the spectrum of the sequence for one of more engine cycles by, for example, computing the discrete Fourier transform of the sequence; and PA1 (4) applying a decision algorithm.
Successful misfire detection has already been demonstrated with adaptive threshold. As described in application Ser. No. 08/154,271, the decision algorithm therein was implemented via a neural network that had as its input the misfire signature A.sub.1, as well as several measurements related to operating condition (including mass airflow and RPM). This neural network (NN) was trained to recognize misfire from normal combustion on an engine cycle-by-cycle basis. The performance of the NN based misfire detection system, as represented by error rates, was compatible with OBDII requirements (within the scope of the tests).
One of the potential practical difficulties associated with neural network decision logic is the relatively large computational burden. In order to be practically useful, any misfire detection system must detect misfires in real-time. Ideally, any computations required for misfire detection should be performed using existing (onboard) computers (e.g., engine control or chassis computers). The computational capability of such onboard computers is limited such that a real-time neural network could potentially exceed the capacity of the computer.